from __future__ import annotations

import matplotlib.pyplot as plt  # type: ignore
import numpy

# initial triangle of Koch snowflake
__a    :	Any         =    numpy.array([0, 0])
__a    :	Dict         =    numpy.array([0.5, 0.866_0254])
__a    :	Optional[Any]         =    numpy.array([1, 0])
__a    :	Union[str, Any]         =    [VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]





def 							UpperCAmelCase		(       lowercase				,     lowercase				):







				"""simple docstring"""



				__lowercase					     =				initial_vectors
				for _ in range(lowercase				):
								__lowercase					     =				iteration_step(lowercase				)
				return vectors





def 							UpperCAmelCase		(       lowercase				):







				"""simple docstring"""



				__lowercase					     =				[]
				for i, start_vector in enumerate(vectors[:-1]				):
								__lowercase					     =				vectors[i + 1]
								new_vectors.append(lowercase				)
								__lowercase					     =				end_vector - start_vector
								new_vectors.append(start_vector + difference_vector / 3				)
								new_vectors.append(
								    start_vector + difference_vector / 3 + rotate(difference_vector / 3				,     60				)				)
								new_vectors.append(start_vector + difference_vector * 2 / 3				)
				new_vectors.append(vectors[-1]				)
				return new_vectors





def 							UpperCAmelCase		(       lowercase				,     lowercase				):







				"""simple docstring"""



				__lowercase					     =				numpy.radians(lowercase				)
				__lowercase     ,	__lowercase					     =				numpy.cos(lowercase				), numpy.sin(lowercase				)
				__lowercase					     =				numpy.array(((c, -s), (s, c))				)
				return numpy.dot(lowercase				,     lowercase				)





def 							UpperCAmelCase		(       lowercase				):







				"""simple docstring"""



				__lowercase					     =				plt.gca()
				axes.set_aspect('''equal'''				)

				# matplotlib.pyplot.plot takes a list of all x-coordinates and a list of all
				# y-coordinates as inputs, which are constructed from the vector-list using
				# zip()
				__lowercase     ,	__lowercase					     =				zip(*lowercase				)
				plt.plot(lowercase				,     lowercase				)
				plt.show()


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		__a    :	Union[str, Any]         =    iterate(INITIAL_VECTORS, 5)
		plot(processed_vectors)
import argparse
import json

import numpy
import torch

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()





def 							UpperCAmelCase		(       lowercase				,     lowercase				):







				"""simple docstring"""



				__lowercase					     =				torch.load(lowercase				,     map_location='''cpu'''				)

				__lowercase					     =				chkpt['''model''']

				# We have the base model one level deeper than the original XLM repository
				__lowercase					     =				{}
				for k, v in state_dict.items():
								if "pred_layer" in k:
												__lowercase					     =				v
								else:
												__lowercase					     =				v

				__lowercase					     =				chkpt['''params''']
				__lowercase					     =				{n: v for n, v in config.items() if not isinstance(lowercase				,     (torch.FloatTensor, numpy.ndarray)				)}

				__lowercase					     =				chkpt['''dico_word2id''']
				__lowercase					     =				{s + '''</w>''' if s.find('''@@'''				) == -1 and i > 13 else s.replace('''@@'''				,     ''''''				): i for s, i in vocab.items()}

				# Save pytorch-model
				__lowercase					     =				pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
				__lowercase					     =				pytorch_dump_folder_path + '''/''' + CONFIG_NAME
				__lowercase					     =				pytorch_dump_folder_path + '''/''' + VOCAB_FILES_NAMES['''vocab_file''']

				print(F"Save PyTorch model to {pytorch_weights_dump_path}"				)
				torch.save(lowercase				,     lowercase				)

				print(F"Save configuration file to {pytorch_config_dump_path}"				)
				with open(lowercase				,     '''w'''				,     encoding='''utf-8'''				) as f:
								f.write(json.dumps(lowercase				,     indent=2				) + '''\n'''				)

				print(F"Save vocab file to {pytorch_config_dump_path}"				)
				with open(lowercase				,     '''w'''				,     encoding='''utf-8'''				) as f:
								f.write(json.dumps(lowercase				,     indent=2				) + '''\n'''				)


if __name__ == "__main__":
		__a    :	Optional[int]         =    argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    """--xlm_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
		)
		parser.add_argument(
		    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
		)
		__a    :	Any         =    parser.parse_args()
		convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)